3 citations · 3 across the 4 of their papers we have counts for
4 papers
In-Context In-Context Learning with Transformer Neural Processes
Matthew Ashman, Cristiana Diaconu, Adrian Weller +1
Neural processes (NPs) are a powerful family of meta-learning models that seek to approximate the posterior predictive map of the ground-truth stochastic process from which each da…
Translation Equivariant Transformer Neural Processes
Matthew Ashman, Cristiana Diaconu, Junhyuck Kim +5
The effectiveness of neural processes (NPs) in modelling posterior prediction maps -- the mapping from data to posterior predictive distributions -- has significantly improved sinc…
Amortised Inference in Neural Networks for Small-Scale Probabilistic Meta-Learning
Matthew Ashman, Tommy Rochussen, Adrian Weller
The global inducing point variational approximation for BNNs is based on using a set of inducing inputs to construct a series of conditional distributions that accurately approxima…
Causal Reasoning in the Presence of Latent Confounders via Neural ADMG Learning
Matthew Ashman, Chao Ma, Agrin Hilmkil +2
Latent confounding has been a long-standing obstacle for causal reasoning from observational data. One popular approach is to model the data using acyclic directed mixed graphs (AD…